Information processing device, information processing method, and information processing system

ABSTRACT

[Object] To analyze a strain of a biological sample more accurately. [Solution] Provided is an information processing device including: a setting unit configured to set at least one region of interest from one captured image constituting a dynamic image for a biological sample; an analysis object specifying unit configured to specify an analysis object for the at least one region of interest; a detection unit configured to detect a motion of the analysis object in the dynamic image; and an analysis unit configured to analyze a strain of the biological sample related to the at least one region of interest on a basis of the detected motion of the analysis object.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Phase of International PatentApplication No. PCT/JP2016/089220 filed on Dec. 29, 2016, which claimspriority benefit of Japanese Patent Application No. JP 2016-045380 filedin the Japan Patent Office on Mar. 9, 2016. Each of the above-referencedapplications is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to an information processing device, aninformation processing method, a program, and an information processingsystem.

BACKGROUND ART

In the fields of medical and life sciences, observation of motions ofmany types of biological samples and evaluation of changes in a formthereof have been performed. For example, a technique of evaluating adynamic characteristic (strain) related to contraction or relaxation ofa biological sample has been developed. By evaluating a strain in thisway, it becomes possible to quantitatively evaluate a dynamiccharacteristic related to a change in a form of the biological sample.

For example, a method of performing a Fourier series expansion of shapeinformation of a cardiac myocyte obtained by performing a segmentationprocess on regions of interest in a captured image corresponding to thecardiac myocyte which is an example of a biological sample and analyzinga strain of the cardiac myocytes on the basis of an obtained Fourierdescriptor is disclosed in the following Non-Patent Literature 1.Further, a method of detecting a motion inside a biological sampledisplayed in a captured image using a correlation function, associatingthe detected motion with a dynamic distortion tensor, and calculating astrain of the biological sample is disclosed in the following Non-PatentLiterature 2.

CITATION LIST Non-Patent Literature

Non-Patent Literature 1, C. Bazan et al. “Image Processing Techniquesfor Assessing Contractility in Isolated Adult Cardiac Myocytes”International Journal of Biomedical Imaging, 2009, 352954.

Non-Patent Literature 2: A. Kamogue et al. “Quantification of cardiacmyocytes contraction based on image correlation analysis.” CytometryPart A, 75A, 2009, p. 298-308.

DISCLOSURE OF INVENTION Technical Problem

However, in the technique disclosed in Non-Patent Literature 1, it isnecessary to perform a process of recognizing a region of interestcorresponding to a cardiac myocyte and a segmentation process for theregion of interest for each frame of the captured image. Therefore,there is a possibility of a load of a strain analysis process becominghuge. Further, in the technique disclosed in Non-Patent Literature 2,since a region corresponding to the biological sample is not specified,it is difficult to acquire the motion inside the biological sample whichchanges the form greatly. Therefore, there is a possibility of theaccuracy of a strain analysis result decreasing depending on thebiological sample to be analyzed.

In this regard, the present disclosure proposes an informationprocessing device, an information processing method, a program, and aninformation processing system which are novel and improved and capableof analyzing a strain of a biological sample more accurately.

Solution to Problem

According to the present disclosure, there is provided an informationprocessing device including: a setting unit configured to set at leastone region of interest from one captured image constituting a dynamicimage for a biological sample; an analysis object specifying unitconfigured to specify an analysis object for the at least one region ofinterest; a detection unit configured to detect a motion of the analysisobject in the dynamic image; and an analysis unit configured to analyzea strain of the biological sample related to the at least one region ofinterest on a basis of the detected motion of the analysis object.

In addition, according to the present disclosure, there is provided aninformation processing method including: setting, by a processor, atleast one region of interest from one captured image constituting adynamic image for a biological sample; specifying, by the processor, ananalysis object for the at least one region of interest; detecting, bythe processor, a motion of the analysis object in the dynamic image; andanalyzing, by the processor, a strain of the biological sample relatedto the at least one region of interest on a basis of the detected motionof the analysis object.

In addition, according to the present disclosure, there is provided aprogram causing a computer to function as: a setting unit configured toset at least one region of interest from one captured image constitutinga dynamic image for a biological sample; an analysis object specifyingunit configured to specify an analysis object for the at least oneregion of interest; a detection unit configured to detect a motion ofthe analysis object in the dynamic image; and an analysis unitconfigured to analyze a strain of the biological sample related to theat least one region of interest on a basis of the detected motion of theanalysis object.

In addition, according to the present disclosure, there is provided aninformation processing system including: an imaging device including animaging unit configured to generate a dynamic image of a biologicalsample; and an information processing device including a setting unitconfigured to set at least one region of interest from one capturedimage constituting the dynamic image, an analysis object specifying unitconfigured to specify an analysis object for the at least one region ofinterest, a detection unit configured to detect a motion of the analysisobject in the dynamic image, and an analysis unit configured to analyzea strain of the biological sample related to the at least one region ofinterest on a basis of the detected motion of the analysis object.

Advantageous Effects of Invention

As described above, according to the present disclosure, it is possibleto analyze a strain of a biological sample more accurately.

Note that the effects described above are not necessarily limitative.With or in the place of the above effects, there may be achieved any oneof the effects described in this specification or other effects that maybe grasped from this specification.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an overview of a configuration of aninformation processing system according to an embodiment of the presentdisclosure.

FIG. 2 is a diagram for describing types of strains according to theembodiment.

FIG. 3 is a functional block diagram illustrating a functionalconfiguration example of an information processing device according tothe embodiment of the present disclosure.

FIG. 4 is a diagram illustrating an example of a region-of-interestsetting method and a tracking point arrangement method of an observationobject performed by a setting unit.

FIG. 5 is a flowchart illustrating a first example of a method by whichan analysis object specifying unit specifies an arrangement position ofa measurement point.

FIG. 6 is a diagram for describing a first example of a method by whichan analysis object specifying unit specifies an arrangement position ofa measurement point.

FIG. 7 is a flowchart illustrating a second example of a method by whichan analysis object specifying unit specifies an arrangement position ofa measurement point.

FIG. 8 is a diagram for describing a second example of a method by whichan analysis object specifying unit specifies an arrangement position ofa measurement point.

FIG. 9 is a diagram for describing an example of a method by which ananalysis object specifying unit specifies an analysis object by ananalysis object specifying unit.

FIG. 10 is a diagram illustrating an example of a block size used in aregion-of-interest motion detection unit and a block size used in ananalysis object motion detection unit.

FIG. 11 is a diagram illustrating an example of a block size used in aregion-of-interest motion detection unit and a block size used in ananalysis object motion detection unit.

FIG. 12 is a diagram illustrating a first example of macro strainanalysis based on a motion vector of a measurement point.

FIG. 13 is a diagram illustrating a second example of macro strainanalysis based on a motion vector of a measurement point.

FIG. 14 illustrates an example of a flowchart illustrating a process ofanalyzing a macro strain using an affine parameter.

FIG. 15 illustrates an example of a flowchart illustrating a process ofanalyzing a micro strain using an affine parameter.

FIG. 16 illustrates an example of a graph illustrating a temporal changein a macro strain.

FIG. 17 is a graph illustrating an example of displacement in a case inwhich motions of measurement points have the same phase and in a case inwhich motions of measurement points have different phases.

FIG. 18 illustrates an example of a flowchart of an imaging process of amicro strain related to contraction or relaxation of an observationobject by a display control unit.

FIG. 19 is a diagram illustrating a process example of an imagingprocess of a micro strain related to contraction or relaxation of anobservation object in a case in which motions of two measurement pointshave the same phase.

FIG. 20 is a diagram illustrating a process example of an imagingprocess of a micro strain related to contraction or relaxation of anobservation object in a case in which motions of two measurement pointshave different phases.

FIG. 21 is a diagram illustrating an example of an imaging process of astrain strength.

FIG. 22 is a flowchart illustrating an example of a process performed byan information processing device according to the embodiment.

FIG. 23 is a flowchart illustrating an example of a process related tostep S511 in a case in which two measurement points are specified asanalysis objects.

FIG. 24 is a flowchart illustrating an example of a process related tostep S511 in a case in which the inside of a region of interest isspecified as an analysis object.

FIG. 25 is a block diagram showing a hardware configuration example ofan information processing device according to an embodiment of thepresent disclosure.

MODE(S) FOR CARRYING OUT THE INVENTION

Hereinafter, (a) preferred embodiment(s) of the present disclosure willbe described in detail with reference to the appended drawings. Notethat, in this specification and the appended drawings, structuralelements that have substantially the same function and structure aredenoted with the same reference numerals, and repeated explanation ofthese structural elements is omitted.

Note that description will be provided in the following order.

-   1. Overview of information processing system-   2. Information processing device-   2.1. Configuration example-   2.2. Process example-   2.3. Effect-   3. Hardware configuration example-   4. Conclusion    «1. Overview of Information Processing System»

FIG. 1 is a diagram showing an overview of a configuration of aninformation processing system 1 according to an embodiment of thepresent disclosure. As shown in FIG. 1, the information processingsystem 1 is provided with an imaging device 10 and an informationprocessing device 20. The imaging device 10 and the informationprocessing device 20 are connected to each other via various types ofwired or wireless networks.

(Imaging device)

The imaging device 10 is a device which generates captured images(dynamic images). The imaging device 10 according to the presentembodiment is realized by, for example, a digital camera. In addition,the imaging device 10 may be realized by any type of device having animaging function, for example, a smartphone, a tablet, a game device, ora wearable device. The imaging device 10 images real spaces usingvarious members, for example, an image sensor such as a charge coupleddevice (CCD) or a complementary metal oxide semiconductor (CMOS), a lensfor controlling formation of a subject image in the image sensor, andthe like. The image sensor and the various members realize the functionof the imaging device 10 as an imaging unit. In addition, the imagingdevice 10 includes a communication device for transmitting and receivingcaptured images and the like to and from the information processingdevice 20. In the present embodiment, the imaging device 10 is providedabove an imaging stage S to image a culture medium M in which a cellthat is an observation object is cultured. Note that the cell is anexample of a biological sample. In addition, the imaging device 10generates dynamic image data by imaging the culture medium M at aspecific frame rate. Note that the imaging device 10 may directly imagethe culture medium M (without involving another member), or may imagethe culture medium M via another member such as a microscope. Inaddition, although the frame rate is not particularly limited, it isdesirable to set the frame rate according to the degree of a change ofthe observation object. Note that the imaging device 10 images a givenimaging region including the culture medium M in order to accuratelytrack a change of the observation object. Dynamic image data generatedby the imaging device 10 is transmitted to the information processingdevice 20.

Note that, although the imaging device 10 is assumed to be a camerainstalled in an optical microscope or the like in the presentembodiment, the present technology is not limited thereto. For example,the imaging device 10 may be an imaging device included in an electronicmicroscope using electron beams such as a scanning electron microscope(SEM) or a transmission electron microscope (TEM), or an imaging deviceincluded in a scanning probe microscope (SPM) that uses a short handsuch as an atomic force microscope (AFM) or a scanning tunnelingmicroscope (STM). In this case, a dynamic image generated by the imagingdevice 10 is a dynamic image obtained by irradiating the observationobject with electron beams in the case of an electronic microscope. Inaddition, when the imaging device 10 is an SPM, a dynamic imagegenerated by the imaging device 10 is a dynamic image obtained bytracing an observation object using a short hand. These dynamic imagescan also be analyzed by the information processing device 20 accordingto the present embodiment.

(Information Processing Device)

The information processing device 20 is a device having an imageanalyzing function. The information processing device 20 is realized byany type of device having an image analyzing function such as a personalcomputer (PC), a tablet, or a smartphone. The information processingdevice 20 includes a processing circuit and a communication device. Forexample, in the information processing device 20 according to thepresent embodiment, the communication device acquires the dynamic imagefrom the imaging device 10 and sets at least one region of interest forthe dynamic image acquired by the processing circuit. Further, theprocessing circuit specifies an analysis object for the set region ofinterest and detects a motion of the analysis object. Further, theprocessing circuit analyzes the strain of the observation object relatedto the region of interest on the basis of the motion of the analysisobject. The processes performed by the processing circuit of theinformation processing device 20 are output to a storage device, adisplay device, or the like provided inside or outside the informationprocessing device 20. Note that the information processing device 20 maybe realized by one or a plurality of information processing devices on anetwork. A functional configuration for realizing the respectivefunctions of the information processing device 20 will be describedbelow.

Note that, although the information processing system 1 is constitutedwith the imaging device 10 and the information processing device 20 inthe present embodiment, the present technology is not limited thereto.For example, the imaging device 10 may perform the processes of theinformation processing device 20 (for example, a detection process andan analysis process). In this case, the information processing system 1can be realized by the imaging device having the detection function andthe analysis function.

Here, the observation object and the strain of the informationprocessing system 1 according to the present embodiment will bedescribed. First, an observation object according to the presentembodiment is mainly a biological sample. A biological sample is anorganism which can be observed using an optical microscope or the like,for example, any of various types of cells, cell organelles orbiological tissues, or living organisms such as micro-organisms orplankton. A biological sample in the present embodiment in particular isan organism that can move in the culture M on the imaging stage S of theimaging device 10. Such a biological sample will be referred tohereinafter as an observation object.

In particular, the observation object according to the presentembodiment may be an observation object that performs periodic movement.The periodic movement may be, for example, movement (a beat) associatedwith contraction and relaxation by a muscle or the like. Examples of theobservation object performing such periodic movement include sarcomeres(sarcomeres) and myogenic fibers, or muscle fibers, muscles, and thelike which are configured of sarcomeres. The muscle may be a skeletalmuscle or a visceral muscle (in particular, an involuntary muscle suchas myocardium). Further, the observation object according to the presentembodiment may be a cardiac myocyte that forms myocardium or a vesselsuch as an artery that beats in accordance with a heartbeat. Further,the application target of the present technology is not limited toobservation objects that perform the periodic movement. For example, anobservation object that performs contractions and relaxation in responseto external stimuli or internal stimuli is included as the applicationtarget of the present technology.

Further, in the present embodiment, the observation object is abiological sample, but the present technology is not limited to thisexample. For example, the observation object may be a structure such asan organism or an inanimate object having a size of a scale ofmillimeters to nanometers. The information processing system 1 may beused to analyze distortion (corresponding to a strain) related to thechange in the form of the observation object as long as it is astructure that performs contraction and relaxation (or elongation).

Next, the strain according to the present embodiment will be described.The strain is an index indicating a dynamic characteristic related to achange in a form of a biological sample (observation object). When theobservation object beats, distortion may occur locally in theobservation object. The distortion is a strain indicating a localdynamic characteristic of the observation object. By quantifying thestrain, it is possible to evaluate a contractile ability of theobservation object. For example, in a case in which the observationobject is a cardiac myocyte, it is possible to quantitatively evaluatean effect related to a medicine administered to the cardiac myocyte, acontractile ability of the cardiac myocytes prepared using a techniquerelated to a regenerative medicine, and the like.

There are roughly two kinds of strains according to the presentembodiment. FIG. 2 is a diagram for describing types of strain accordingto the present embodiment. Referring to FIG. 2, an observation object500 contracts and changes its shape as shown by an observation object501. At this time, there are two types of strains of the observationobject 500, that is, macro strains MaS1 and MaS2 indicating a dynamiccharacteristic related to contraction or relaxation of the entireobservation object 500 and micro strains MiS1 and MiS2 indicating alocal dynamic characteristic inside the observation object 500.

The macro strains are strains indicating the magnitude of the change inthe form of the observation object 500 in an expansion and contractiondirection of the observation object 500. In other words, the macrostrains are strains calculated on the basis of a difference (distortion)between an original form of the observation object 500 and a form of theobservation object 501 contracted from the original form.

On the other hand, the micro strains are strain indicating changeamounts in local motions of the observation object 500 that contributeto the movement in the expansion and contraction direction of theobservation object 500. In other words, as illustrated in FIG. 2, themicro strains are strains calculated on the basis of changes inindividual motions inside the observation object 500 that contribute tothe movement in the expansion and contraction direction of theobservation object 500.

The macro strains are so-called non-dimensional distortion amounts,whereas the micro strains are values having a dimension of a changeamount of motion in two dimensions (that is, corresponding toacceleration). Further, the micro strains according to the presentembodiment are vectors (having a size and a direction). According to theinformation processing system 1 of the present embodiment, it isnecessary to complexly evaluate the local dynamic characteristics of theobservation object in further detail using at least one of the two typesof strains.

The overview of the information processing system 1 according to anembodiment of the present disclosure has been described above. Theinformation processing device 20 included in the information processingsystem 1 according to an embodiment of the present disclosure isrealized in the following embodiment. A specific configuration exampleand a process example of the information processing device 20 will bedescribed below. Further, in the following description, macro strainsand micro strains are referred to collectively as “strains” unless it isparticularly necessary to distinguish them.

«2. Information Processing Device»

Hereinafter, the information processing device 20 according to anembodiment of the present disclosure will be described with reference toFIGS. 3 to 24.

<2.1. Configuration Example>

FIG. 3 is a functional block diagram illustrating a functionalconfiguration example of the information processing device 20 accordingto one embodiment of the present disclosure. As illustrated in FIG. 3,the information processing device 20 according to the present embodimentincludes a control unit 200, a communication unit 210, and a storageunit 220. A function of the control unit 200 is implemented by aprocessing circuit such as a central processing unit (CPU) installed inthe information processing device 20. Further, a function of thecommunication unit 210 is implemented by a communication deviceinstalled in the information processing device 20. Further, a functionof the storage unit 220 is implemented by a storage device such as astorage installed in the information processing device 20. Therespective function units will be described below.

(Control Unit)

The control unit 200 controls the overall operation of the informationprocessing device 20. Further, as illustrated in FIG. 3, the controlunit 200 includes functions of a setting unit 201, an analysis objectspecifying unit 202, a detection unit 203, an analysis unit 204, and adisplay control unit 205, and undertakes an operation of the informationprocessing device 20 according to the present embodiment. The functionsof the respective function units installed in the control unit 200 willbe described later.

(Communication Unit)

The communication unit 210 is a communication section that theinformation processing device 20 has, and performs various types ofcommunication with external devices in a wireless or a wired manner viaa network (or directly). For example, the communication unit 210performs communication with the imaging device 10. More specifically,the communication unit 210 acquires a dynamic image generated by theimaging device 10. In addition, the communication unit 210 may performcommunication with devices other than the imaging device 10. Forexample, the communication unit 210 may transmit information related toan analysis result obtained from the analysis unit 204 to be describedlater, information related to display of an analysis result obtainedfrom the display control unit 205, or the like to an externalinformation processing device, a display device, or the like.

(Storage Unit)

The storage unit 220 is a storage device installed in the informationprocessing device 20 and stores information acquired by thecommunication unit 210, information obtained by the respective functionunits of the control unit 200, and the like. Further, the storage unit220 appropriately outputs the stored information in response to arequest from each function unit of the control unit 200 or from thecommunication unit 210.

Next, the functions of the respective function units installed in thecontrol unit 200 will be described.

(Setting Unit)

The setting unit 201 sets at least one region of interest from onecaptured image constituting a dynamic image that the communication unit210 acquires from the imaging device 10. Note that a region of interestrefers to a region used to estimate a motion of an observation object inthe present specification. This region of interest may not necessarilycoincide with a region corresponding to an observation object (forexample, a biological sample such as a cell) in a dynamic image (whichwill be referred to hereinafter as an observation object region). Forexample, the region of interest according to the present embodiment isdescribed as being set in a region formed by a closed curvecorresponding to a contour of the observation object, but the region ofinterest may be set in a region corresponding to tissue inside theobservation object.

Further, the region of interest according to the present embodiment isdescribed as being a region surrounded by a closed curve (a curve inwhich a starting point coincides with an ending point), but the regionof interest may be a region indicated by an open curve (including astraight line). Further, a plurality of regions may be set as the regionof interest, or a region indicated by a shape such as a FIG. 8 may beset.

In addition, a region of interest may be set through an operation of auser using the information processing device 20, or automaticallydetected from a dynamic image by the setting unit 201 using a techniquesuch as image analysis. In the case of the latter, the setting unit 201may detect an observation object region through image analysis. Forexample, the setting unit 201 may set a region of interest according toa type of observation object.

In addition, the setting unit 201 may set one or a plurality of regionsof interest from one captured image. For example, when a plurality ofobservation objects are included in one captured image, the setting unit201 may set regions of interest for the respective observation objectsfor comparison of motions of these observation objects. Accordingly, therespective motions of the plurality of observation objects can beestimated and each strain can be analyzed, and therefore the results ofthe analysis can be compared.

Note that the one captured image may be a captured image equivalent to afirst frame of a dynamic image that the communication unit 210 acquires.By setting a region of interest for the captured image of the firstframe, the position of the region of interest in the first frame can bea reference when, for example, motions of a region of interest areanalyzed in a dynamic image in a time series manner. Thus, the result ofthe analysis becomes more accurate than when a position of a region ofinterest of an arbitrary captured image is set as a reference. Further,a captured image may be a captured image in a frame corresponding to astart point of analysis of the strain of the observation object by theanalysis unit 204 to be described later. Accordingly, it is possible todetect the motion on the basis of a reference form of the observationobject by the detection unit 203 to be described later.

In addition, when a region of interest is set in one captured image, thesetting unit 201 according to the present embodiment may dispose aplurality of tracking points for the region of interest. A trackingpoint mentioned in the present specification is a point disposed tocorrespond to a region of interest set in a given captured image. In thepresent embodiment, for example, tracking points are disposed on a lineor a contour defining a region of interest with predetermined intervals.The detection unit 203 to be described below detects positions of thetracking points in another captured image captured at a different timepoint from the captured image used when the region of interest is set.The detection unit 203 can detect a motion of the region of interestbased on movement positions of these tracking points.

In addition, the number of tracking points disposed and dispositionintervals thereof may be decided according to the type of observationobject or the shape of a region of interest. For example, when the shapeof the region of interest significantly changes, it is desirable toincrease the number of the tracking points disposed and reduce theirdisposition intervals. Accordingly, even if the form of a cellsignificantly changes, the change in the form of the cell can be trackedwith high accuracy. In addition, in order to reduce a load ofcalculation, it is desirable to reduce the number of the tracking pointsdisposed and increase their disposition intervals.

Here, a region-of-interest setting method and a tracking pointarrangement method performed by the setting unit 201 according to thepresent embodiment will be described. FIG. 4 is a diagram illustratingan example of a region-of-interest setting method and a tracking pointarrangement method for the observation object performed by the settingunit 201. Referring to FIG. 4, an observation object region 1000corresponding to an image of an observation object is assumed to beincluded in a captured image. In this case, as illustrated in FIG. 4,the setting unit 201 may set the observation object region 1000 as aregion of interest 1100. In this case, a contour line of the region ofinterest 1100 may be a contour line of the observation object region1000 (that is, a boundary line between the observation object region1000 and a non-observation object region). Then, the setting unit 201may arrange a plurality of tracking points CP on the contour line of theobservation object region 1000 (that is, the contour line of the regionof interest 1100).

Further, the region of interest 1100 illustrated in FIG. 4 may be, forexample, a region corresponding to a part of the tissue or the likeincluded in the observation object. More specifically, in a case inwhich a beat of a part of the tissue included in the observation objectis considered to contribute to the dynamic characteristic related to thecontraction and relaxation of the observation object, the setting unit201 may set a region corresponding to a part of the tissue as the regionof interest. Accordingly, it is possible to detect the dynamiccharacteristic of the tissue corresponding to a desired region, and itis possible to suppress a computational cost by reducing the settingsize of the region of interest to the minimum necessary.

Information related to the region of interest set by the setting unit201 is output to the analysis object specifying unit 202 and thedetection unit 203.

(Analysis Object Specifying Unit)

The analysis object specifying unit 202 specifies an analysis object forat least one region of interest. The analysis object is used when theanalysis unit 204 to be described later analyzes the strain. Theanalysis object may be decided in accordance with selection of the useror in accordance with a type of strain (for example, the macro strain orthe micro strain) analyzed by the analysis unit 204 or the like.

For example, the analysis object according to the present embodiment is(1) two measurement points arranged on the contour line of the region ofinterest or (2) the inside of the region of interest. The analysisobject specifying unit 202 specifies either or both of the above (1) and(2) as the analysis object. A method by which the analysis objectspecifying unit 202 specifies the analysis object related to the above(1) and (2) will be described below.

(1) Method of Specifying Two Measurement Points

The analysis object specifying unit 202 may arrange two measurementpoints on the contour line of the region of interest as the analysisobject. The two measurement points are used to analyze the strainindicating the dynamic characteristic related to the contraction orrelaxation of the entire observation object, that is, the macro strain.Therefore, the two measurement points can be arranged at positionscorresponding to a portion of the contour line of the region of interestin which the motion is largest in the observation object when theobservation object contracts or relaxes. The arrangement positions ofthe two measurement points are considered to be (a) a position at whichthe two points are arranged as far apart as possible on the contour lineof the region of interest or (b) a position on the contour line of theregion of interest at which the motion of the two points is largest.Regarding the above (a), for example, the analysis object specifyingunit 202 may specify the arrangement position of the measurement pointon the basis of the shape of the contour line of the region of interest.Further, regarding the above (b), for example, the analysis objectspecifying unit 202 may specify the arrangement position of themeasurement point on the basis of a change in a dynamic image of thecontour line of the region of interest (that is, a change in the shapeof the region of interest). A specific example of the method by whichthe analysis object specifying unit 202 specifies the arrangementposition of the measurement point will be described below.

First, a method by which the analysis object specifying unit 202specifies the arrangement position of the measurement point in the caseof the above (a) will be described. FIG. 5 is a flowchart illustrating afirst example of the method by which the analysis object specifying unit202 specifies the arrangement position of the measurement point.Further, FIG. 6 is a diagram for describing the first example of themethod by which the analysis object specifying unit 202 specifies thearrangement position of the measurement point. First, the analysisobject specifying unit 202 calculates center coordinates of the regionof interest (S101 in FIG. 5). For example, in a case in which the regionof interest 1100 and a plurality of tracking points CP are arranged forthe observation object as illustrated in a schematic diagram F61 of FIG.6, the analysis object specifying unit 202 calculates coordinates of acenter point 1101 of the region of interest 1100 (see a schematicdiagram F62 in FIG. 6). The center point 1101 may be calculated, forexample, as a weighted average of the coordinates of a plurality oftracking points CP arranged in the region of interest 1100. Further, thecenter point 1101 may be calculated using a known method of obtainingthe center coordinates.

Then, the analysis object specifying unit 202 specifies a position of apoint farthest from the center point 1101 on the contour line of theregion of interest 1100 as an arrangement position of a firstmeasurement point SP1 (S103 in FIG. 5). For example, as illustrated in aschematic diagram F63 in FIG. 6, the analysis object specifying unit 202may specify a position of a point at which a distance D1 from the centerpoint 1101 on the contour line of the region of interest 1100 is largestas the arrangement position of the first measurement point SP1 which isthe analysis object. Further, the analysis object specifying unit 202may specify a point farthest from the center point 1101 among arbitrarypositions on the contour line of the region of interest 1100 as thefirst measurement point SP1 or may specify the tracking point CPfarthest from the center point 1101 among the tracking points CParranged on the contour line as the first measurement point SP1 asillustrated in a schematic diagram F63 of FIG. 6.

Then, the analysis object specifying unit 202 specifies a position ofthe point farthest from the first measurement point SP1 on the contourline of the region of interest 1100 as an arrangement position of asecond measurement point SP2 (S105 in FIG. 5). For example, asillustrated in a schematic diagram F64 of FIG. 6, the analysis objectspecifying unit 202 may specify a position of the point at which adistance D2 from the first measurement point SP1 on the contour line ofthe region of interest 1100 is largest as the arrangement position ofthe second measurement point SP2 which is the analysis object. Further,the analysis object specifying unit 202 may specify a point farthestfrom the first measurement point SP1 among arbitrary positions on thecontour line of the region of interest 1100 as the second measurementpoint SP2 or may specify the tracking point CP farthest from the firstmeasurement point SP1 among the tracking points CP arranged on thecontour line as the second measurement point SP2 as illustrated in aschematic diagram F64 of FIG. 6.

In general, the motion related to the contraction and relaxation of theobservation object related to the region of interest often occurs in alongitudinal direction of the observation object. Therefore, it ispossible to specify the measurement point on the basis of the centerposition in the shape of the contour line of the region of interest andanalyze a motion of a portion which is largest in the motion of theobservation object. Therefore, it is possible to analyze the macrostrain of the observation object related to the region of interest witha high degree of accuracy. Further, in the example illustrated in FIGS.5 and 6, the measurement point is specified on the basis of the centerposition of the region of interest, but the present technology is notlimited to this example. For example, the analysis object specifyingunit 202 may estimate two points at which a distance between two pointsamong arbitrary two points on the contour line of the region of interestis largest from the shape of the contour line and specify the estimatedtwo points as the measurement point.

Next, a method by which the analysis object specifying unit 202specifies the arrangement position of the measurement point in the caseof the above (b) will be described. FIG. 7 is a flowchart illustrating asecond example of the method by which the analysis object specifyingunit 202 specifies the arrangement position of the measurement point.Further, FIG. 8 is a diagram for describing the second example of themethod by which the analysis object specifying unit 202 specifies thearrangement position of the measurement point. First, before theanalysis object specifying process by the analysis object specifyingunit 202, the detection unit 203 detects a motion vector of the trackingpoint arranged on the contour line of the region of interest (S201 inFIG. 7). For example, in a case in which the region of interest 1100 anda plurality of tracking points CP are arranged for the observationobject as illustrated in a schematic diagram F81 in FIG. 8, thedetection unit 203 calculates a motion vector MV of each tracking pointCP. Further, the method of calculating the motion vector MV will bedescribed later. Further, a motion to be detected here is the motionvector MV calculated on the basis of the motion at two consecutive timeswhen the observation object contracts or relaxes, but the presenttechnology is not limited to this example. For example, a motion to bedetected may be a displacement amount calculated on the basis of themagnitude of the motion of each tracking point CP when one period ofcontraction and relaxation is performed. Here, as will be described indetail later, the detection unit 203 may rearrange the tracking point atan appropriate position for the region of interest after the movement.In this case, there is a possibility that the position of the trackingpoint CP is changed appropriately. Therefore, in a case in which themeasurement point is set on the basis of the displacement amount relatedto the tracking point CP, the rearrangement of the tracking point CP bythe detection unit 203 may not be performed.

Then, the analysis object specifying unit 202 specifies the trackingpoint CP having the largest detected motion among the detected trackingpoints CP as a first measurement point SP3 (S203 in FIG. 7). Forexample, as illustrated in a schematic diagram F81 of FIG. 8, theanalysis object specifying unit 202 may specify the tracking point CPindicating the largest motion vector MVa among the motion vectors MVcalculated by the detection unit 203 as the first measurement point SP3(see a schematic diagram F82 of FIG. 8).

Then, the analysis object specifying unit 202 specifies a position ofthe point farthest away from the first measurement point SP3 on thecontour line of the region of interest 1100 as an arrangement positionof a second measurement point SP4 (S205 in FIG. 7). For example, asillustrated in a schematic diagram F83 of FIG. 8, the analysis objectspecifying unit 202 may specify a position of a point at which adistance D3 from the first measurement point SP3 on the contour line ofthe region of interest 1100 is largest as the arrangement position ofthe second measurement point SP4 which is the analysis object. Further,the analysis object specifying unit 202 may specify a point farthestfrom the first measurement point SP3 among arbitrary positions on thecontour line of the region of interest 1100 as the second measurementpoint SP4 or may specify the tracking point CP farthest from the firstmeasurement point SP3 among the tracking points CP arranged on thecontour line as the second measurement point SP4 as illustrated in aschematic diagram F83 of FIG. 8.

As described above, it is possible to specify the position which islargest in motion in the contour line of the region of interest as themeasurement point and analyze a motion of a portion which is largest inthe motion of the observation object. Therefore, it is possible toanalyze the macro strain of the observation object related to the regionof interest with a high degree of accuracy.

Further, it is preferable that the measurement point specified once befixed in a period in which it is a strain analysis object. This is toconsecutively measure the motion of the measurement point through singleanalysis.

Further, the tracking region of the specified measurement point (theregion centered on the measurement point which is taken into account inthe detection of the motion of the measurement point by the detectionunit 203 to be described later) is set to a predetermined size.

(2) Specifying Method in Case in which Inside of Region of Interest isAnalysis Object

Further, the analysis object specifying unit 202 may specify the insideof the region of interest as the analysis object. At this time, forexample, the analysis object specifying unit 202 performs a segmentationprocess on a captured image using the region of interest in order todetect the motion inside the region of interest specified as theanalysis object. The segmentation process is a process of extracting animage of a portion corresponding to the region of interest from thecaptured image.

FIG. 9 is a diagram for describing an example of the method ofspecifying the analysis object by the analysis object specifying unit202. Referring to a schematic diagram F91 and a schematic diagram F92 ofFIG. 9, the analysis object specifying unit 202 generates a mask 1110with the region of interest 1100 set for the observation object region1000 as a closed region. The segmentation process is performed byapplying the mask 1110 to the captured image for the observation objectregion 1000.

Then, the analysis object specifying unit 202 cuts a mesh 1120 insidethe region of interest 1100 after the segmentation process (a meshprocessing. See a schematic diagram F93 of FIG. 9). Motion vectors MV3and MV4 detected for the respective meshes 1120 are detected by thedetection unit 203 as the motion of the analysis object. In other words,the motion inside the region of interest in the present embodimentcorresponds to a motion of each mesh.

Further, the region of interest moves or is deformed depending on thechange in the form of the observation object. In other words, thegenerated mask 1110 may be generated by the analysis object specifyingunit 202 for each movement or deformation of the region of interest (forexample, for each captured image). In other words, the segmentationprocess is performed on the region of interest in which the motiondetection result detected by the detection unit 203 to be describedlater is reflected. The segmentation process related to the generationof the mask 1110 is not performed on the basis of the image recognitionof the observation object included in the captured image but performedon the basis of the process of detecting the motion of the region ofinterest. Therefore, since a processing load related to the imagerecognition does not occur, the computational cost can be suppressed.

Further, the analysis object specifying unit 202 may specify only a partof the inside of the region of interest as the analysis object. Forexample, the analysis object specifying unit 202 may specify only aregion corresponding to a part of the region of interest in which thechange in the form of the corresponding observation object is large inthe inside of the region of interest as the analysis object.Accordingly, since the motion detection process is not performed on apart with a relatively small change in form, the computational cost issuppressed.

The analysis object specified by the analysis object specifying unit 202may be both the two measurement points and the inside of the region ofinterest. By specifying a plurality of analysis objects, it becomespossible to comprehensively evaluate the strains for the observationobjects using the results of the macro strains and the micro strains.

The analysis object specifying unit 202 outputs information related tothe specified analysis object to the detection unit 203.

(Detection Unit)

The detection unit 203 detects at least motion of the analysis object inthe dynamic image specified by the analysis object specifying unit 202.Further, the detection unit 203 may detect the motion of the region ofinterest in the dynamic image. As illustrated in FIG. 3, the detectionunit 203 includes a region-of-interest motion detection unit 231 and ananalysis object motion detection unit 232.

—Region-of-interest Motion Detection Unit

The region-of-interest motion detection unit 231 has a function ofdetecting the motion of region of interest. For example, theregion-of-interest motion detection unit 231 detects the motion of theregion of interest in another captured image having a differentcapturing time point from one captured image constituting the dynamicimage in one captured image. More specifically, the region-of-interestmotion detection unit 231 first detects the motions of the respectivetracking points arranged for the region of interest, and detects themotion of region of interest on the basis of the estimated motions ofrespective tracking points.

First, the region-of-interest motion detection unit 231 according to thepresent embodiment detects the motions of the tracking points disposedfor the region of interest set by the setting unit 220, and therebyestimates the motion of the region of interest. Specifically, theregion-of-interest motion detection unit 231, at first, estimatespositions of the tracking points that have been disposed in one capturedimage in another captured image of which the capturing time point isdifferent from the one captured image. The other captured image may be acaptured image of any frame among a few frames before and after theframe of the one captured image. The region-of-interest motion detectionunit 231 detects the motions of the tracking points in the dynamic imageby performing a process for estimating positions of the tracking pointsin another captured image for respective captured images constitutingthe dynamic image. Further, the motion detected by theregion-of-interest motion detection unit 231 may be a motion in theentire dynamic image or a part of the dynamic image.

The region-of-interest motion detection unit 231 may estimate positionsof the tracking points based on, for example, a motion vector calculatedby comparing a captured image to another captured image. This motionvector may be a motion vector calculated for each tracking point. Themotion vector may be calculated using a technique such as blockmatching, or a gradient method. The region-of-interest motion detectionunit 231 according to the present embodiment is described as estimatingthe motion vector using block matching.

For example, with regard to a tracking region in a predetermined sizeincluding tracking points, the region-of-interest motion detection unit231 may estimate positions of the tracking points in the other capturedimage by detecting a region of which information of pixels included inthe tracking region of the captured image matches that of the othercaptured image from a predetermined block size (search range) of theother captured image. In this case, a size of the tracking region andthe block size may be decided according to an imaging condition (forexample, an imaging magnification) of the imaging device 10, the type ofthe observation object, the type of analysis performed on theobservation object. When a movement of the observation object is large,for example, the tracking region or the block size may be set to belarger. Accordingly, accuracy in estimation of tracking points by theregion-of-interest motion detection unit 231 can be enhanced. Inaddition, when there are a number of tracking points for a region ofinterest, the tracking region or the block size may be adjusted to besmall in order to reduce a load of calculation.

In addition, the region-of-interest motion detection unit 231 mayestimate a position of a tracking point in the other captured imagegenerated at an imaging time point decided based on information of theobservation object. When a change in the morphology of an observationobject of which a speed of the change in the morphology is slow istracked, for example, a difference in captured images between aplurality of consecutive frames generated by the imaging device 10 issmall. For this reason, when a change in the shape of an observationobject of which a speed of the change in the shape is slow is tracked,the region-of-interest motion detection unit 231 may perform a detectionprocess with a captured image a number of frames before or after theframe of the captured image as the other captured image. To be morespecific, the region-of-interest motion detection unit 231 may perform adetection process with a captured image a number frames after thecaptured image as the other captured image. The frame interval betweenthe captured image and the other captured image enables the data amountof the captured image that is subject to a tracking process to bereduced. Accordingly, it is possible to reduce a load of calculation andtrack a motion of the region of interest over a long period of time. Theframe interval can be appropriately set according to the type, a state,or the like of the observation object.

The region-of-interest motion detection unit 231 further detects themotion of the region of interest (for example, the movement of region ofinterest or the change in the shape of the contour line of the region ofinterest) on the basis of the movement positions of the detectedtracking points. Accordingly, it is possible to track the change in theform of the observation object related to the region of interest.Further, the region-of-interest motion detection unit 231 may rearrangethe tracking points for the region of interest after the motiondetection. Accordingly, the estimation accuracy of the motion of theregion of interest can be improved.

The information related to the motion of the region of interest may beoutput to the analysis object specifying unit 202. Accordingly, in acase in which the analysis object specifying unit 202 specifies theinside of the region of interest as the analysis object, thesegmentation process can be performed in accordance with the motion ofthe region of interest.

—Analysis Object Motion Detection Unit

The analysis object motion detection unit 232 has a function ofdetecting the motion of the analysis object. For example, the analysisobject motion detection unit 232 detects the motion of the analysisobject in another captured image having a different capturing time pointfrom one captured image constituting the dynamic image in one capturedimage. More specifically, the analysis object motion detection unit 232may estimate the motion of the analysis object on the basis of themotion vector calculated by comparing one captured image with anothercaptured image. The detected motion vector is implemented by a methodsimilar to the method performed by the region-of-interest motiondetection unit 231 such as block matching. The analysis object motiondetection unit 232 according to the present embodiment is assumed todetect the motion vector of the analysis object through the blockmatching. Further, the motion detected by the analysis object motiondetection unit 232 may be a motion in the entire dynamic image or a partof the dynamic image.

For example, in a case in which the measurement point is arranged on thecontour line of the region of interest by the analysis object specifyingunit 202, the analysis object motion detection unit 232 detects themotion vector of the measurement point in the dynamic image. Further, ina case in which the analysis object specifying unit 202 specifies theinside of the region of interest as the analysis object, the analysisobject motion detection unit 232 detects the motion vectors of therespective meshes included in the region of interest.

Further, in the present embodiment, a detector used in theregion-of-interest motion detection unit 231 may be different from adetector used in the analysis object motion detection unit 232. Forexample, in a case in which the detector for detecting the motion vectorby the block matching is used in the region-of-interest motion detectionunit 231 and the analysis object motion detection unit 232, the blocksize may be different between the region-of-interest motion detectionunit 231 and the analysis object motion detection unit 232.

FIGS. 10 and 11 are diagrams illustrating examples of a block size usedin the region-of-interest motion detection unit 231 and a block sizeused in the analysis object motion detection unit 232. FIG. 10illustrates an example in which the measurement point is arranged on thecontour line of the region of interest as the analysis object, and FIG.11 illustrates an example in which the inside of the region of interestis specified as the analysis object.

As illustrated in FIG. 10, for example, block sizes B1 and B2 of themeasurement points SP1 and SP2 may be equal to or larger than a blocksize B3 of the tracking point CP1. The measurement points SP1 and SP2are larger in a change in position than the other tracking points CP.Therefore, the block sizes of the measurement points SP1 and SP2 may beset to be equal to or larger than the block size of the tracking pointCP in order to more reliably detect the change in shape caused by thecontraction or relaxation of the observation object.

Further, as illustrated in FIG. 11, for example, a block size B4 of themesh 1120 may be equal to or smaller than the block size B3 of thetracking point CP1. In a case in which the block size B4 is set to betoo large, there is a possibility that an image pattern close to animage pattern included in the mesh 1120 before the motion detection isdetected in a portion other than a portion near the mesh 1120.

Further, the sizes of the block sizes may be appropriately changed inaccordance with a type of captured image (a bright field image, a phasedifference image, or the like) for the observation object.

Further, in the above example, the block size is assumed to be differentbetween the region-of-interest motion detection unit 231 and theanalysis object motion detection unit 232, but the present technology isnot limited to this example. For example, the size of the trackingregion may be different between the region-of-interest motion detectionunit 231 and the analysis object motion detection unit 232.

As described above, since the different detectors are used in theregion-of-interest motion detection unit 231 and the analysis objectmotion detection unit 232, the motion detection according to thecharacteristic of each motion can be performed. Accordingly, theaccuracy of motion detection is improved.

The region-of-interest motion detection unit 231 detects the motion ofthe region of interest for each captured image constituting the dynamicimage. Further, the analysis object motion detection unit 232 detectsthe motion of the analysis object for each captured image. Then, theanalysis object motion detection unit 232 outputs the informationrelated to the motion of the detected analysis object to the analysisunit 204. Further, the information related to the detection result ofthe region of interest and the motion of the analysis object by thedetection unit 203 is output to the display control unit 205.

(Analysis Unit)

The analysis unit 204 analyzes the strain of the observation objectrelated to at least one region of interest on the basis of the motion ofthe analysis object. The analysis unit 204 according to the presentembodiment analyzes at least either the macro strain or the micro strainon the basis of the motion vector of at least one of the measurementpoint or the inside (mesh) of the region of interest specified by theanalysis object specifying unit 202. An example of the analysis processby the analysis unit 204 will be described below.

—Macro Strain Analysis Based on Motion of Measurement Point

The analysis unit 204 may perform the macro strain analysis on theobservation object on the basis of the motion vector of the measurementpoint specified on the contour line of the region of interest by theanalysis object specifying unit 202. FIG. 12 is a diagram illustrating afirst example of the macro strain analysis based on the motion vector ofthe measurement point. As illustrated in a schematic diagram F121 inFIG. 12, the analysis unit 204 first calculates a distance L(t₀) of aline segment L0 connecting measurement points SP1 and SP2. This distanceL(t₀) is calculated from positions of the measurement points SP1 and SP2in a captured image captured at a time different from a time at whichthe observation object contracts or relaxes.

When the observation object contracts or relaxes (contracts in aschematic diagram F122 of FIG. 12), motion vectors MV1 and MV2 of themeasurement points SP1 and SP2 in the dynamic image are detected. Atthis time, a distance L(t) of a line segment L1 connecting measurementpoints SP1′ and SP2′ is shorter than the distance L(t₀) (see a schematicdiagram F123 of FIG. 12). Further, the position after the movement ofthe measurement point is estimated on the basis of the motion vectordetected by the detection unit 203 (the analysis object motion detectionunit 232). In this case, a macro strain 40 is indicated by the followingFormula (1).

[Math.  1]                                        $\begin{matrix}{{ɛ(t)} = \frac{{L\left( t_{0} \right)} - {L(t)}}{L\left( t_{0} \right)}} & (1)\end{matrix}$

The analysis unit 204 analyzes the macro strain 40 over time by trackingthe changes in the positions of the measurement points SP1 and SP2.

Further, the method of calculating the macro strain ε(t) is not limitedto the example illustrated in FIG. 12. FIG. 13 is a diagram illustratinga second example of the macro strain analysis based on the motion vectorof the measurement point. As illustrated in a schematic diagram F131 ofFIG. 13, the analysis unit 204 first calculates a distance L(t₀) of aline segment L0 connecting measurement points SP1 and SP2. This distanceL(t₀) is calculated from positions of the measurement points SP1 and SP2in the captured image captured at a time different from a time at whichthe observation object contracts or relaxes. The analysis unit 204 setsthe line segment L0 as a reference line.

When the observation object contracts or relaxes., the motion vectors ofthe measurement points SP1 and SP2 in the dynamic image are detected(see a schematic diagram F132 of FIG. 13). If a line segment connectingthe measurement points SP1′ and SP2′ at this time is a line segment L2(see a schematic diagram F133 of FIG. 13), the distance L(t) describedabove may be a length when the line segment L2 is projected onto thereference line L0. In a case in which the directions of the motionvectors MV1 and MV2 of the measurement points SP1 and SP2 are notparallel to the reference line L0, a part of force related to thecontraction and relaxation of the observation object is considered tocontribute the change in the direction of the reference line L0, thatis, the change in the enlargement and contraction direction of theobservation object. By projecting the line segment L2 onto the referenceline L0, it is possible to analyze the macro strain contributing to thechange in the enlargement and contraction direction of the observationobject.

—Micro Strain Analysis Based on Motion of Mesh

In a case in which the analysis object specifying unit 202 specifies theinside of the region of interest as the analysis object, the analysisunit 204 may perform the micro strain analysis on the observation objecton the basis of the motion vector of the inside of the region ofinterest. Specifically, the analysis unit 204 may analyze a time changeamount (that is, acceleration) of the motion vector of each mesh as themicro strain. The micro strain to be analyzed here is a micro strain ofeach mesh. Accordingly, it is possible to obtain a partial strain of theobservation object corresponding to the region of interest. In otherwords, it is possible to detect a local dynamic characteristic of thecontraction and relaxation of observation object in further detail.

Further, by applying a Green-Lagrange distortion tensor to the microstrain obtained for each mesh, the strain serving as the local dynamiccharacteristic in the region in which the micro strain is analyzed canbe obtained. Further, a statistical value such as an average value, amedian value, a maximum value, or a minimum value of the micro strainobtained for each mesh can be a value indicating the local dynamiccharacteristic in the region in which the micro strain is analyzed.

—Strain Analysis Using Affine Parameter

Further, in a case in which the analysis object specifying unit 202specifies the inside of the region of interest as the analysis object,the analysis unit 204 may calculate an affine parameter from the motionvector of the inside of the region of interest and analyze the strain(the macro strain or the micro strain) on the basis of the affineparameter. The affine parameter according to the present embodiment isobtained by applying a least squares technique to the motion vectors ofeach mesh. By using the affine parameter, it is possible to analyze boththe macro strain and the micro strain.

FIG. 14 is an example of a flowchart illustrating a process of analyzingthe macro strain using the affine parameter. Referring to FIG. 14,first, the analysis unit 204 acquires the motion vector of the inside(each mesh) of the region of interest (S301). Then, the analysis unit204 calculates the affine parameter for the obtained motion vector usingthe least squares technique (S303). Then, the analysis unit 204 extractsa parameter related to the enlargement and contraction and thedistortion from the affine parameter (S305). The parameter related tothe enlargement and contraction and the distortion is associated withthe enlargement and contraction and the distortion of the region ofinterest in the longitudinal direction. In other words, the parameter isa parameter associated with the contraction and relaxation of theobservation object. Then, the analysis unit 204 analyzes the macrostrain of the observation object from the parameter related to theenlargement and contraction and the distortion (S307). Specifically, theanalysis unit 204 calculates the time change amount of the parameterrelated to the enlargement and contraction and the distortion as ananalysis value of the macro strain.

By obtaining the parameter related to the enlargement and contractionand the distortion of the entire region of interest, it is possible toanalyze the macro strain on the basis of the motion vector of the insideof the region of interest without specifying the measurement point onthe contour line of the region of interest. Accordingly, it is possibleto analyze both the macro strain and the micro strain on the basis ofonly the motion in the region of interest.

FIG. 15 is an example of a flowchart illustrating a process of analyzingthe micro strain using the affine parameter. Referring to FIG. 15,first, the analysis unit 204 acquires the motion vector of the inside(each mesh) of the region of interest (S311). Then, the analysis unit204 calculates the affine parameter for the obtained motion vector usingthe least squares technique (S313). Then, the analysis unit 204 convertsthe affine parameter into a motion vector of each pixel of the inside ofthe region of interest (S315). Then, the analysis unit 204 performs themicro strain analysis on each pixel on the basis of the motion vector ofeach pixel (S317). A micro strain analysis method is similar to theabove-described micro strain analysis method of each mesh.

By converting the affine parameter into the motion vector of each pixel,it is possible to perform the micro strain analysis in finer units thanmeshes. Accordingly, it is possible to obtain information related to themicro strain of the observation object in further detail.

The analysis unit 204 outputs the information related to the analyzedstrain to the display control unit 205.

(Display Control Unit)

The display control unit 205 controls display of the information relatedto the analyzed strain. For example, the display control unit 205 has afunction of displaying the information related to the macro strains ormicro strains analyzed for the observation object in various displayforms such as a graphs, imaging, or a table. The display controlled bythe display control unit 205 is displayed on a screen of a displaydevice (not illustrated) or the like. A screen display example by thedisplay control unit 205 will be described below.

—Graph Indicating Temporal Change in Macro Strain

FIG. 16 is an example of a graph illustrating a temporal change in themacro strain. As illustrated in FIG. 16, the macro strain ε analyzed bythe analysis unit 204 may be displayed as a time-series graph. Bydisplaying the macro strain ε as the graph, it is possible toquantitatively evaluate not only the beat period of the observationobject but also a characteristic of the change in the form of theobservation object at the time of contraction and at the time ofrelaxation.

—Imaging of Contraction/relaxation

For example, the display control unit 205 may display an arrowindicating a size and a direction thereof as the micro strain.Specifically, as illustrated in FIG. 3, the arrow indicating the microstrain may be superimposed on the region of interest. Accordingly, it ispossible to detect a strength and a direction in which the localdistortion of the observation object by the beat occurs.

Further, the display control unit 205 may display the informationrelated to the micro strain by imaging such as color mapping. Forexample, the display control unit 205 may render a portion in which themicro strain is analyzed when the observation object contracts orrelaxes with a rendering color associated with the contraction state orthe relaxation state of the observation object. In this case, thedisplay control unit 205 determines whether or not the observationobject is contracting or relaxing on the basis of the motion of theregion of interest or the motion of the analysis object. The displaycontrol unit 205 according to the present embodiment determines whetheror not the observation object is contracting or relaxing on the basis ofthe motions of the two measurement points.

However, motions related to contraction or relaxation of end portions ofthe observation object in the length direction (portions in which amotion by contraction and relaxation is largest and which corresponds tothe measurement points) may have the same phase or different phases,depending on the observation object. FIG. 17 is a graph illustrating anexample of displacement in a case in which the motions of themeasurement points have the same phase and in a case in which themotions of the measurement points have different phases. Further, agraph related to the displacement can be obtained, for example, byintegrating the motion vectors of the measurement points.

It is possible to determine whether or not the motions of the twomeasurement points have the same phase on the basis of peak positions ofthe temporal change in distances from stationary positions of the twomeasurement points. For example, if the measurement point SP1 and themeasurement point SP2 have the same peak position, the motions of thetwo measurement points have the same phase (see a graph G171 of FIG.17), and if there is a deviation between the peak positions, the motionsof the two measurement points have different phases (see a graph G172 ofFIG. 17).

In a case in which the motions of the two measurement points have thesame phase, timings at which the observation object contracts or relaxesare totally identical. On the other hand, in a case in which the motionsof the two measurement points have different phases, timings at whichthe observation object contracts or relaxes are different at the endportion in the longitudinal direction. In this case, for example, it ispossible to estimate the position of the beat center of the observationobject and determine whether a part of the observation objectcorresponding to each mesh is contracting or relaxing on the basis ofthe position of the beat center and the direction of the motion vectorof each mesh. More specifically, if an inner product of a direction ofthe mesh to the beat center and a direction of the motion vector of eachmesh is positive, it is possible to determine that the part of theobservation object corresponding to each mesh is contracting.Conversely, if the inner product of the direction of the mesh to thebeat center and the direction of the motion vector of each mesh isnegative, it is possible to determine that the part of the observationobject corresponding to each mesh is relaxing.

Further, a process related to the estimation of the beat center isimplemented by a known technique. For example, the process may beimplemented by a technique disclosed in JP 2014-75999 A.

Next, the flow of an imaging process of the micro strain related to thecontraction or relaxation of the observation object will be described.FIG. 18 illustrates an example of a flowchart of an imaging process ofthe micro strain related to the contraction or relaxation of theobservation object by the display control unit 205. Referring to FIG.18, first, the display control unit 205 acquires the temporal changes inthe distances from the stationary positions of the two measurementpoints (S401). Then, the display control unit 205 detects the peakpositions of the temporal changes of the two measurement points (S403).Then, the display control unit 205 determines whether the motions of thetwo measurement points have the same phase or different phases from thedetection result of the peak positions (S405). In a case in which themotions have different phases (YES in S405), the display control unit205 estimates the beat center of the observation object (S407).

Then, the display control unit 205 performs the imaging process on themicro strain related to the contraction or relaxation (S409). First, animaging process in a case in which the motions of the two measurementpoints have the same phase will be described.

FIG. 19 is a diagram illustrating a process example of the imagingprocess of the micro strain related to the contraction or relaxation ofthe observation object in a case in which the motions of two measurementpoints have the same phase. A schematic diagram F191 of FIG. 19 is anexample of the imaging process in a case in which the observation objectcontracts. Further, a schematic diagram F192 of FIG. 19 is an example ofthe imaging process in a case in which the observation object relaxes.

Referring to the schematic diagram F191, in a case in which theobservation object contracts, each of the micro strains at the endportions of the region of interest 1100 indicates the contractiondirection. In this case, the display control unit 205 may render regions1131 a and 1131 b in which the micro strain is analyzed with a renderingcolor indicating the contraction, and reflect the regions 1131 a and1131 b in a screen 300. Further, referring to the schematic diagramF192, in a case in which the observation object relaxes, each of themicro strains at the end portions of the region of interest 1100 mayoccur in the relaxation direction. In this case, the display controlunit 205 may render regions 1132 a and 1132 b in which the micro strainis analyzed with a rendering color indicating the relaxation, andreflect the regions 1132 a and 1132 b in the screen 300. Since therendering color of the region in which the micro strain is analyzed isdifferent between when the observation object contracts and when theobservation object relaxes, the user can easily determine the regionindicating the dynamic characteristic contributing to the contraction orrelaxation of the observation object.

Next, an imaging process in a case in which the motions of the twomeasurement points have different phases will be described.

FIG. 20 is a diagram illustrating a process example of the imagingprocess of the micro strain according to the contraction state or therelaxation state of the observation object in a case in which themotions of two measurement points have different phases. A schematicdiagram F201 in FIG. 20 is an example of the imaging process in a casein which one end of the observation object contracts. Further, aschematic diagram F202 in FIG. 20 is an example of the imaging processin a case in which one end of the observation object relaxes.

Referring to the schematic diagram F201, a beat center 1140 is assumedto have been estimated for the region of interest 1100 in step S407 ofFIG. 18. At this time, the display control unit 205 determines that adirection of an analyzed micro strain MiS3 is a direction toward thebeat center 1140. In other words, the display control unit 205determines that the micro strain MiS3 is a micro strain related to thecontraction of the observation object. In this case, the display controlunit 205 may render a region 1141 in which the micro strain is analyzedwith a rendering color indicating the contraction and reflect the region1141 in the screen 300.

Further, referring to a schematic diagram F202, the display control unit205 determines that a direction of an analyzed micro strain MiS4 is adirection away from the beat center 1140. In other words, the displaycontrol unit 205 determines that the micro strain MiS4 is a micro strainrelated to the relaxation of the observation object. In this case, thedisplay control unit 205 may render a region 1142 in which the microstrain is analyzed with a rendering color indicating the relaxation, andreflect the region 1142 in the screen 300.

As described above, by using the beat center, even when the motions ofthe end portions of the observation object have different phases, theregion contributing to the contraction and relaxation of the observationobject can be presented to the user. Further, the beat center 1140illustrated in FIG. 20 may be displayed or may not be displayed on thescreen 300.

Further, in a case in which the motions of the end portions of theobservation object when the observation object contracts or relaxes aredetected to have the same phase in advance, the determination of whetherthe observation object is contracting or relaxing is not limited to theexample in which it is performed using the two measurement points. Forexample, the display control unit 205 may control the display form ofthe information related to the strain on the basis of the change in theshape of the contour line of the region of interest.

Specifically, it may be determined whether the observation object iscontracting or relaxing on the basis of the temporal change (shapedifferential) in the length of the contour line of the region ofinterest or the area surrounded by the contour line, and the renderingof the micro strain illustrated in FIG. 19 may be controlled on thebasis of the determination result. More specifically, in a case in whichthe length of the contour line is decreasing chronologically, theobservation object is considered to be contracting. Further, in a casein which the length of the contour line is increasing chronologically,the observation object is considered to be relaxing. As described above,it is possible to determine the contraction state or the relaxationstate of the observation object on the basis of the change in the shapeof the contour line of the region of interest. Therefore, it is possibleto control a rendering form related to the micro strain on the basis ofthe change in the shape of the contour line.

—Imaging of Strain Strength

Further, the display control unit 205 may control the display form ofthe information related the strain on the basis of the strength (size)of the micro strain. For example, the display control unit 205 mayrender a portion in which the micro strain is analyzed when theobservation object contracts or relaxes with a rendering colorassociated with the strength of the micro strain.

FIG. 21 is a diagram illustrating an example of the imaging process ofthe strain strength. Referring to FIG. 21, the display control unit 205renders regions 1150 a and 1150 b in which the micro strain is analyzedwith a rendering color corresponding to the size of the micro strain inthe screen 300. Accordingly, it is possible to intuitively recognize thedistribution of the micro strain in the region contributing to thecontraction or relaxation of the observation object.

Further, in the imaging process of the micro strain according to thecontraction state or the relaxation state of the observation objectdescribed above, the display control unit 205 may control a contrastingdensity of the rendering color associated with the contraction state orthe relaxation state in accordance with the strength (size) of the microstrain. Accordingly, it is possible to obtain the strength distributionof the micro strain in the region illustrating the dynamiccharacteristic contributing to the contraction or relaxation of theobservation object.

The display control example by the display control unit 205 has beendescribed above. Further, the information related to the analyzed strainmay be output to another display device, a storage device, or the likevia the communication unit 210 or may be stored in the storage unit 220.

Further, the number of observation objects in which the strain isanalyzed by the information processing device 20 according to thepresent embodiment is not particularly limited. For example, in a casein which images of a plurality of observation objects are included inthe dynamic image, the region of interest may be set in each of aplurality of observation objects, the analysis object may be specifiedfor each of a plurality of regions of interest, the motion of each ofthe specified analysis objects may be detected, and the strain of eachof the observation objects may be analyzed on the basis of each motion.In this case, the strain analyzed for each observation object may benormalized. The size of the strain also differs in accordance with thesize of the observation object. By normalizing the strain, it becomespossible to compare the analysis results of the strain or the like amonga plurality of observation objects.

<2.2. Process Example>

The configuration and functions of the information processing device 20according to one embodiment of the present disclosure have beendescribed above. Next, an example of a process by the informationprocessing device 20 according to one embodiment of the presentdisclosure will be described with reference to FIGS. 22 to 24.

FIG. 22 is a flowchart illustrating an example of a process by theinformation processing device 20 according to one embodiment of thepresent disclosure. First, the control unit 200 acquires dynamic imagedata from the imaging device 10 via the communication unit 210 (S501).

Then, the setting unit 201 extracts one captured image from the acquireddynamic image data and sets at least one region of interest from onecaptured image (S503). Then, the setting unit 201 arranges the trackingpoints on the contour line of the region of interest (S505).

Then, the analysis object specifying unit 202 specifies the analysisobject for the region of interest (S507). The analysis object specifiedhere is at least either of the two measurement points or the inside(mesh) of the region of interest. Further, in a case in which theanalysis object specifying unit 202 specifies the two measurement pointson the basis of the change in the shape of the region of interest, theanalysis object is not specified at a time point of step S507. Instead,in step S603 to be described later, the two measurement points servingas the analysis objects are specified.

Then, the detection unit 203 (the region-of-interest motion detectionunit 231) detects the motion of the region of interest in the dynamicimage (S509). Then, the detection unit 203 (the analysis object motiondetection unit 232) detects the motion of the analysis object in thedynamic image, and the analysis unit 204 analyzes the strain on thebasis of the detected motion of the analysis object (S511). Here,content of the process in step S511 changes, depending on the analysisobject specified in step S507.

First, an example in which the analysis object specifying unit 202specifies the two measurement points as the analysis object will bedescribed. FIG. 23 is a flowchart illustrating an example of a processrelated to step S511 in a case in which the two measurement points arespecified as the analysis object. Referring to FIG. 23, first, in stepS507, a process in a case in which the analysis object is not specifiedis performed (S601). More specifically, in a case in which the analysisobject specifying unit 202 does not specify the two measurement pointsserving as the analysis object in step S507 (NO in S601), the analysisobject specifying unit 202 specifies the two measurement points on thebasis of the change in the shape of the region of interest (S603). Then,the analysis object motion detection unit 232 detects the motion vectorsof the two measurement points (S605). Then, the analysis unit 204analyzes the macro strain of the observation object on the basis of thedetected motion vectors of the two measurement points (S607).

Next, an example in which the analysis object specifying unit 202specifies the inside (mesh) of the region of interest as the analysisobject will be described. FIG. 24 is a flowchart illustrating an exampleof a process related to step S511 in a case in which the inside ofregion of interest is specified as the analysis object. Referring toFIG. 24, first, the analysis object specifying unit 202 performs thesegmentation process (and the mesh process) using the region of interestin which the detection result by the detection unit 203 is reflected,and specifies the inside of the region of interest as the analysisobject (S611). Then, the detection unit 203 detects the motion vector ofthe inside of the region of interest (S613). Then, the analysis unit 204analyzes the macro strain or the micro strain of the observation objecton the basis of the motion vector of the inside of the detected regionof interest (S615).

The specific examples of the process related to step S511 have beendescribed above. Referring again to FIG. 22, the display control unit205 controls the display of the information related to the strainanalyzed by the analysis unit 204 (S513). Thereafter, the control unit200 determines whether or not the analysis processes ends (S515). In acase in which the analysis process continues (NO in S515), the processrelated to step S509 is performed again.

<2.3. Effect>

The configuration example and the process examples of the informationprocessing device 20 according to one embodiment of the presentdisclosure have been described above. The information processing device20 according to the present embodiment specifies the analysis object forthe region of interest, detects the motion of the specified analysisobject, and analyzes the strain of the observation object related to theregion of interest on the basis of the detected motion of the analysisobject. With this configuration, it is possible to analyze both themacro strain and the micro strain related to the contraction orrelaxation of the observation object by detecting the motion of theanalysis object specified for the observation object and tracking themotion. Accordingly, it is possible to obtain the strain related to theperiodic change in the form of the observation object and the strain ofthe region contributing to the change in the form of the observationobject. Therefore, it is possible to analyze the strain of observationobject with a high degree of accuracy.

Further, the information processing device 20 according to the presentembodiment can analyze the strain while tracking the change in the formof the observation object by detecting the tracking points and themotion of the analysis object. In other words, it is possible to analyzethe strain without performing direct image recognition on theobservation object for each captured image. Therefore, the computationalcost can be suppressed.

Further, the information processing device 20 according to the presentembodiment can control the display form of the information related tothe analyzed strain on the basis of the motion of the analysis object orthe motion of the region of interest. Accordingly, since the displayform of the information related to the strain changes in accordance withthe contraction or relaxation of the observation object, it is possibleto easily detect a relation between the strain and the beat of theobservation object.

«3. Hardware Configuration Example»

Next, with reference to FIG. 25, a hardware configuration of aninformation processing device according to an embodiment of the presentdisclosure is described. FIG. 25 is a block diagram showing a hardwareconfiguration example of the information processing device according tothe embodiment of the present disclosure. An illustrated informationprocessing device 900 can realize the information processing device 20in the above described embodiment.

The information processing device 900 includes a CPU 901, read onlymemory (ROM) 903, and random access memory (RAM) 905. In addition, theinformation processing device 900 may include a host bus 907, a bridge909, an external bus 911, an interface 913, an input device 915, anoutput device 917, a storage device 919, a drive 921, a connection port925, and a communication device 929. The information processing device900 may include a processing circuit such as a digital signal processor(DSP) or an application-specific integrated circuit (ASIC), instead ofor in addition to the CPU 901.

The CPU 901 functions as an arithmetic processing device and a controldevice, and controls the overall operation or a part of the operation ofthe information processing device 900 according to various programsrecorded in the ROM 903, the RAM 905, the storage device 919, or aremovable recording medium 923. For example, the CPU 901 controlsoverall operations of respective function units included in theinformation processing device 20 of the above-described embodiment. TheROM 903 stores programs, operation parameters, and the like used by theCPU 901. The RAM 905 transiently stores programs used when the CPU 901is executed, and parameters that change as appropriate when executingsuch programs. The CPU 901, the ROM 903, and the RAM 905 are connectedwith each other via the host bus 907 configured from an internal bussuch as a CPU bus or the like. The host bus 907 is connected to theexternal bus 911 such as a Peripheral Component Interconnect/Interface(PCI) bus via the bridge 909.

The input device 915 is a device operated by a user such as a mouse, akeyboard, a touchscreen, a button, a switch, and a lever. The inputdevice 915 may be a remote control device that uses, for example,infrared radiation and another type of radio waves. Alternatively, theinput device 915 may be an external connection device 927 such as amobile phone that corresponds to an operation of the informationprocessing device 900. The input device 915 includes an input controlcircuit that generates input signals on the basis of information whichis input by a user to output the generated input signals to the CPU 901.The user inputs various types of data and indicates a processingoperation to the information processing device 900 by operating theinput device 915.

The output device 917 includes a device that can visually or audiblyreport acquired information to a user. The output device 917 may be, forexample, a display device such as an LCD, a PDP, and an OLED, an audiooutput device such as a speaker and a headphone, and a printer. Theoutput device 917 outputs a result obtained through a process performedby the information processing device 900, in the form of text or videosuch as an image, or sounds such as audio sounds.

The storage device 919 is a device for data storage that is an exampleof a storage unit of the information processing device 900. The storagedevice 919 includes, for example, a magnetic storage device such as ahard disk drive (HDD), a semiconductor storage device, an opticalstorage device, or a magneto-optical storage device. The storage device919 stores therein the programs and various data executed by the CPU901, and various data acquired from an outside. Further, the storagedevice 919 can realize the function of the storage unit 220 according tothe above embodiments.

The drive 921 is a reader/writer for the removable recording medium 923such as a magnetic disk, an optical disc, a magneto-optical disk, and asemiconductor memory, and built in or externally attached to theinformation processing device 900. The drive 921 reads out informationrecorded on the mounted removable recording medium 923, and outputs theinformation to the RAM 905. The drive 921 writes the record into themounted removable recording medium 923.

The connection port 925 is a port used to directly connect devices tothe information processing device 900. The connection port 925 may be aUniversal Serial Bus (USB) port, an IEEE1394 port, or a Small ComputerSystem Interface (SCSI) port, for example. The connection port 925 mayalso be an RS-232C port, an optical audio terminal, a High-DefinitionMultimedia Interface (HDMI (registered trademark)) port, and so on. Theconnection of the external connection device 927 to the connection port925 makes it possible to exchange various kinds of data between theinformation processing device 900 and the external connection device927.

The communication device 929 is a communication interface including, forexample, a communication device for connection to a communicationnetwork NW. The communication device 929 may be, for example, a wired orwireless local area network (LAN), Bluetooth (registered trademark), ora communication card for a wireless USB (WUSB). The communication device929 may also be, for example, a router for optical communication, arouter for asymmetric digital subscriber line (ADSL), or a modem forvarious types of communication. For example, the communication device929 transmits and receives signals in the Internet or transits signalsto and receives signals from another communication device by using apredetermined protocol such as TCP/IP. The communication network NW towhich the communication device 929 connects is a network establishedthrough wired or wireless connection. The communication network NW is,for example, the Internet, a home LAN, infrared communication, radiowave communication, or satellite communication. Further, at least one ofthe connection port 925 and the communication device 929 can realize thefunction of the communication unit 210 according to the aboveembodiments.

The example of the hardware configuration of the information processingdevice 900 has been introduced.

«4. Conclusion»

The preferred embodiment(s) of the present disclosure has/have beendescribed above with reference to the accompanying drawings, whilst thepresent disclosure is not limited to the above examples. A personskilled in the art may find various alterations and modifications withinthe scope of the appended claims, and it should be understood that theywill naturally come under the technical scope of the present disclosure.

For example, although the information processing system 1 is configuredto be provided with the imaging device 10 and information processingdevice 20 in the above-described embodiment, the present technology isnot limited thereto. For example, the imaging device 10 may have thefunction of the information processing device 20 (a setting function, ananalysis object specifying function, a detection function, and ananalysis function). In this case, the information processing system 1 isrealized by the imaging device 10. In addition, the informationprocessing device 20 may have the function of the imaging device 10(imaging function). In this case, the information processing system 1 isrealized by the information processing device 20. Further, the imagingdevice 10 may have a part of the function of the information processingdevice 20, and the information processing device 20 may have a part ofthe function of the imaging device 10.

The steps in the processes performed by the information processingdevice in the present specification may not necessarily be processedchronologically in the orders described in the flowcharts. For example,the steps in the processes performed by the information processingdevice may be processed in different orders from the orders described inthe flowcharts or may be processed in parallel.

Also, a computer program causing hardware such as the CPU, the ROM, andthe RAM included in the information processing device to carry out theequivalent functions as the above-described configuration of theinformation processing device can be generated. Also, a storage mediumhaving the computer program stored therein can be provided.

Further, the effects described in this specification are merelyillustrative or exemplified effects, and are not limitative. That is,with or in the place of the above effects, the technology according tothe present disclosure may achieve other effects that are clear to thoseskilled in the art from the description of this specification.

Additionally, the present technology may also be configured as below.

(1)

An information processing device, including:

a setting unit configured to set at least one region of interest fromone captured image constituting a dynamic image for a biological sample;

an analysis object specifying unit configured to specify an analysisobject for the at least one region of interest;

a detection unit configured to detect a motion of the analysis object inthe dynamic image; and

an analysis unit configured to analyze a strain of the biological samplerelated to the at least one region of interest on a basis of thedetected motion of the analysis object.

(2)

The information processing device according to (1), in which theanalysis object specifying unit specifies two measurement points on acontour line of the at least one region of interest, as the analysisobject,

the detection unit detects motions of the two measurement points in thedynamic image, and

the analysis unit analyzes the strain on a basis of the motions of thetwo measurement points.

(3)

The information processing device according to (2), in which theanalysis object specifying unit specifies arrangement positions of thetwo measurement points on a basis of a shape of the contour line.

(4)

The information processing device according to (2) or (3), in which theanalysis object specifying unit specifies arrangement positions of thetwo measurement points on a basis of a change in a shape of the contourline in the dynamic image.

(5)

The information processing device according to any one of (1) to (4), inwhich the analysis object specifying unit specifies an inside of the atleast one region of interest as the analysis object,

the detection unit detects a motion of the inside of the at least oneregion of interest in the dynamic image, and

the analysis unit analyzes the strain on a basis of the motion of theinside of the at least one region of interest.

(6)

The information processing device according to (5), in which theanalysis unit calculates an affine parameter of the motion of the insideof the at least one region of interest detected by the detection unitand analyzes the strain on a basis of the affine parameter.

(7)

The information processing device according to any one of (1) to (6), inwhich the detection unit is able to further detect a motion of the atleast one region of interest, and

in the detection unit, a motion detector used for detecting the motionof the analysis object is different from a motion detector used fordetecting the motion of the at least one region of interest.

(8)

The information processing device according to any one of (1) to (7), inwhich the analysis unit analyzes the strain on a basis of a temporalchange in the motion of the analysis object.

(9)

The information processing device according to any one of (1) to (8),further including

a display control unit configured to control display of informationrelated to the strain analyzed by the analysis unit.

(10)

The information processing device according to (9), in which the displaycontrol unit controls a display form of the information related to thestrain in accordance with the motion of the analysis object.

(11)

The information processing device according to (10), in which thedisplay control unit controls the display form of information related tothe strain by using a beat center estimated for the at least one regionof interest.

(12)

The information processing device according to any one of (9) to (11),in which the display control unit controls the display form of theinformation related to the strain in accordance with a change in a shapeof the contour line of the at least one region of interest in thedynamic image.

(13)

The information processing device according to any one of (9) to (12),in which the display control unit controls the display form of theinformation related to the strain on a basis of a magnitude of themotion of the analysis object.

(14)

The information processing device according to any one of (1) to (13),in which the strain includes a strain indicating a dynamiccharacteristic related to contraction or relaxation of the entirebiological sample.

(15)

The information processing device according to any one of (1) to (14),in which the strain includes a strain indicating a local dynamiccharacteristic inside the biological sample.

(16)

The information processing device according to any one of (1) to (15),in which the setting unit sets a region corresponding to the biologicalsample included in the one captured image as the at least one region ofinterest.

(17)

The information processing device according to any one of (1) to (16),in which the biological sample is a biological sample that performsperiodic movement.

(18)

An information processing method, including:

setting, by a processor, at least one region of interest from onecaptured image constituting a dynamic image for a biological sample;

specifying, by the processor, an analysis object for the at least oneregion of interest;

detecting, by the processor, a motion of the analysis object in thedynamic image; and

analyzing, by the processor, a strain of the biological sample relatedto the at least one region of interest on a basis of the detected motionof the analysis object.

(19)

A program causing a computer to function as:

a setting unit configured to set at least one region of interest fromone captured image constituting a dynamic image for a biological sample;

an analysis object specifying unit configured to specify an analysisobject for the at least one region of interest;

a detection unit configured to detect a motion of the analysis object inthe dynamic image; and

an analysis unit configured to analyze a strain of the biological samplerelated to the at least one region of interest on a basis of thedetected motion of the analysis object.

(20)

An information processing system, including:

an imaging device including

-   -   an imaging unit configured to generate a dynamic image of a        biological sample; and

an information processing device including

-   -   a setting unit configured to set at least one region of interest        from one captured image constituting the dynamic image,    -   an analysis object specifying unit configured to specify an        analysis object for the at least one region of interest,    -   a detection unit configured to detect a motion of the analysis        object in the dynamic image, and    -   an analysis unit configured to analyze a strain of the        biological sample related to the at least one region of interest        on a basis of the detected motion of the analysis object.

REFERENCE SIGNS LIST

-   1 information processing system-   10 imaging device-   20 information processing device-   200 control unit-   201 setting unit-   202 analysis object specifying unit-   203 detection unit-   204 analysis unit-   205 display control unit-   210 communication unit-   220 storage unit-   231 region-of-interest motion detection unit-   232 analysis object motion detection unit

The invention claimed is:
 1. An information processing device,comprising: a processor configured to: set at least one region ofinterest from a first captured image of a dynamic image of a biologicalsample; specify an analysis object for the at least one region ofinterest; detect a motion of the analysis object in the dynamic imagebased on the at least one region of interest from the first capturedimage as a reference for a second captured image of the dynamic image;analyze a strain of the biological sample related to the at least oneregion of interest based on the detected motion of the analysis object;and control display of information related to the strain across aplurality of regions of the biological sample, such that a strength ofthe strain in a first region of the plurality of regions is visuallydistinguishable from the strength of the strain in a second region ofthe plurality of regions.
 2. The information processing device accordingto claim 1, wherein the processor is further configured to: specify twomeasurement points on a contour line of the at least one region ofinterest, as the analysis object; detect motions of the two measurementpoints in the dynamic image; and analyze the strain based on the motionsof the two measurement points.
 3. The information processing deviceaccording to claim 2, wherein the processor is further configured tospecify arrangement positions of the two measurement points based on ashape of the contour line.
 4. The information processing deviceaccording to claim 2, wherein the processor is further configured tospecify arrangement positions of the two measurement points based on achange in a shape of the contour line in the dynamic image.
 5. Theinformation processing device according to claim 1, wherein theprocessor is further configured to: specify an inside of the at leastone region of interest as the analysis object; detect a motion of theinside of the at least one region of interest in the dynamic image; andanalyze the strain based on the motion of the inside of the at least oneregion of interest.
 6. The information processing device according toclaim 5, wherein the processor is further configured to: calculate anaffine parameter of the motion of the inside of the at least one regionof interest; and analyze the strain based on the affine parameter. 7.The information processing device according to claim 1, wherein theprocessor is further configured to detect a motion of the at least oneregion of interest, and a motion detector used for the detection of themotion of the analysis object is different from a motion detector usedfor detection of the motion of the at least one region of interest. 8.The information processing device according to claim 1, wherein theprocessor is configured to analyze the strain based on a temporal changein the motion of the analysis object.
 9. The information processingdevice according to claim 1, further comprising a display deviceconfigured to display the information related to the strain.
 10. Theinformation processing device according to claim 9, wherein theprocessor is further configured to control a display form of theinformation related to the strain in accordance with the motion of theanalysis object.
 11. The information processing device according toclaim 10, wherein the processor is further configured to control thedisplay form of the information related to the strain based on a beatcenter estimated for the at least one region of interest.
 12. Theinformation processing device according to claim 10, wherein theprocessor is further configured to control the display form of theinformation related to the strain in accordance with a change in a shapeof a contour line of the at least one region of interest in the dynamicimage.
 13. The information processing device according to claim 10,wherein the processor is further configured to control the display formof the information related to the strain based on a magnitude of themotion of the analysis object.
 14. The information processing deviceaccording to claim 1, wherein the strain indicates a dynamiccharacteristic related to contraction or relaxation of the biologicalsample.
 15. The information processing device according to claim 1,wherein the strain indicates a local dynamic characteristic inside thebiological sample.
 16. The information processing device according toclaim 1, wherein the processor is further configured to set a regioncorresponding to the biological sample included in the first capturedimage as the at least one region of interest.
 17. The informationprocessing device according to claim 1, wherein the biological sample isa biological sample that performs periodic movement.
 18. An informationprocessing method, comprising: setting, by a processor, at least oneregion of interest from a first captured image of a dynamic image of abiological sample; specifying, by the processor, an analysis object forthe at least one region of interest; detecting, by the processor, amotion of the analysis object in the dynamic image based on the at leastone region of interest from the first captured image as a reference fora second captured image of the dynamic image; analyzing, by theprocessor, a strain of the biological sample related to the at least oneregion of interest based on the detected motion of the analysis object;and controlling, by the processor, display of information related to thestrain across a plurality of regions of the biological sample, such thata strength of the strain in a first region of the plurality of regionsis visually distinguishable from the strength of the strain in a secondregion of the plurality of regions.
 19. A non-transitorycomputer-readable medium having stored thereon, computer-executableinstructions which, when executed by a computer, cause the computer toexecute operations, the operations comprising: setting at least oneregion of interest from a first captured image of a dynamic image of abiological sample; specifying an analysis object for the at least oneregion of interest; detecting a motion of the analysis object in thedynamic image based on the at least one region of interest from thefirst captured image as a reference for a second captured image of thedynamic image; analyzing a strain of the biological sample related tothe at least one region of interest based on the detected motion of theanalysis object; and controlling display of information related to thestrain across a plurality of regions of the biological sample, such thata strength of the strain in a first region of the plurality of regionsis visually distinguishable from the strength of the strain in a secondregion of the plurality of regions.
 20. An information processingsystem, comprising: an imaging device configured to generate a dynamicimage of a biological sample; and an information processing deviceconfigured to: set at least one region of interest from a first capturedimage of the dynamic image; specify an analysis object for the at leastone region of interests; detect a motion of the analysis object in thedynamic image based on the at least one region of interest from thefirst captured image as a reference for a second captured image of thedynamic image; and analyze a strain of the biological sample related tothe at least one region of interest based on the detected motion of theanalysis object; and control display of information related to thestrain across a plurality of regions of the biological sample, such thata strength of the strain in a first region of the plurality of regionsis visually distinguishable from the strength of the strain in a secondregion of the plurality of regions.